裂缝天线缝制造精度对电性能影响的预测

为了避免传统方法在分析结构因素对电性能影响时需要假设和近似处理的弊端,提出了根据平板裂缝天线生产制造中的数据,使用支持向量回归建立缝制造精度对电性能指标影响的预测方法。给出了一种以最小化支持向量回归的拟合能力和泛化能力为目标函数来选择支持向量回归机参数的方法,以方便该方法的工程应用。案例研究表明,该方法能够较准确地预测缝制造精度对电性能的影响;与传统的BP神经网络和最小二乘法对比,该方法具有更好的泛化能力,可以应用到平板裂缝天线的计算机辅助制造中。     

《搜神记》中的韵文研究

六朝志怪小说的代表作《搜神记》在写作上采用了韵散相间的叙述方式,其中的韵文可分为:谣谚;预言、卜辞和隐语;诗歌。分言之,谣谚的征引使小说既保留了民间传说固有的风调,又使作品的思想得以升华;预言、卜辞和隐语的使用,来源于国人原始信仰的积淀,在小说中作为推动故事发展的关键,反映了其时的社会文化心理;作品中还有一些赋诗、引诗的情况,这种承自上古文学传统的表达方式,提高了小说的文体地位,增强了小说的鉴赏性。概言之,韵散相间的写作方式具有深刻的文体意义,为后世小说的创作提供了借鉴。     

Extended scaled prediction variance optimality for modified central composite design

Robust parameter designs (RPDs) enable the experimenter to discover how to modify the design of the product to minimize the effect due to variation from noise sources. The aim of this article is to show how this amount of work can be reduced under modified central composite design (MCCD). We propose a measure of extended scaled prediction variance (ESPV) for evaluation of RPDs on MCCD. Using these measures, we show that we can check the error or bias associated with estimating the model parameters and suggest the values of α recommended for MCCS under minimum ESPV.     

Statistical inference for the Gompertz distribution based on Type-II progressively hybrid censored data

In this article, the statistical inference for the Gompertz distribution based on Type-II progressively hybrid censored data is discussed. The estimation of the parameters for Gompertz distribution is obtained using maximum likelihood method (MLE) and Bayesian method under three different loss functions. We also proved the existence and uniqueness of the MLE. The one-sample Bayesian prediction intervals are obtained. The work is done for different values of the parameters. We apply the Monto Carlo simulation to compare the proposed methods, also an example is discussed to construct the Prediction intervals.     

Bayesian prediction of order statistics with fixed and random sample sizes based on k-record values from Pareto distribution

In this paper, the two-parameter Pareto distribution is considered and the problem of prediction of order statistics from a future sample and that of its geometric mean are discussed. The Bayesian approach is applied to construct predictors based on observed k-record values for the cases when the future sample size is fixed and when it is random. Several Bayesian prediction intervals are derived. Finally, the results of a simulation study and a numerical example are presented for illustrating all the inferential procedures developed here.     

Wiki-worthy: collective judgment of candidate notability

The use of socio-technical data to predict elections is a growing research area. We argue that election prediction research suffers from under-specified theoretical models that do not properly distinguish between ‘poll-like’ and ‘prediction market-like’ mechanisms understand findings. More specifically, we argue that, in systems with strong norms and reputational feedback mechanisms, individuals have market-like incentives to bias content creation toward candidates they expect will win. We provide evidence for the merits of this approach using the creation of Wikipedia pages for candidates in the 2010 US and UK national legislative elections. We find that Wikipedia editors are more likely to create Wikipedia pages for challengers who have a better chance of defeating their incumbent opponent and that the timing of these page creations coincides with periods when collective expectations for the candidate's success are relatively high.     

数控加工与编程数字化教学资源建设

为培养高职数控技术专业学生的数控加工与编程能力,通过调研典型制造型企业,以其岗位能力需求为基础,以企业的实践过程为任务流程,规划了数控加工与编程课程的教学体系,并构建了基于网络化教学平台的数字化教学资源库。     

Conditional covariance penalties for mixed models

The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross-validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial-, the t-, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia.